Online learning systems represent a wide range of methods for electronic delivery of information in an education or training set-up. More specifically, interactive online learning systems are revolutionizing the way education is imparted. Such interactive online learning systems offer an alternate platform that is not only faster and potentially better but also bridges the accessibility and affordability barriers for the users. Moreover, the online learning systems provide users with the flexibility of being in any geographic location while participating in the session.
Apart from providing convenience and flexibility, such online learning systems also ensure more effective and engaging interactions in a comfortable learning environment. With advancement of technology, personalized interactive sessions are provided according to specific needs rather than just following a set pattern of delivering knowledge as prescribed by conventional educational institutions. Each session is tailor-made to meet the needs of the specific user. Moreover, such system allows a mobile learning environment where learning is not time bound (anywhere-anytime learning).
However, there is a need to monitor such interactions and to measure the efficiency of such online learning systems. There is also a need to classify users for a more accurate matching process, which in turn will improve the quality of education. Currently the effectiveness of such interactive learning systems is manually reviewed. Such manual interventions could be time consuming and less scalable. Moreover, reviews done in such manner lead to subjective and inaccurate ratings.
Therefore, an accurate and highly scalable system is required, for measuring the effectiveness and engagement of an interaction session conducted on an online learning system.